APPLICATION DRAMA TECHNIQUE IN DEVELOPING SPEAKING OF THE ELEVENTH GRADE STUDENTS OF MAN MAJENE
Bibliographic record
Abstract
This research aims to proof whether the application of drama technique is able to develop students’ speaking skill.The design of this study is experimental design. There were two groups: experimental and control group. The research was conducted at Madrasah Aliah Negeri Majene, and took 40 students as samples. The data were collected by using the pre-test, the treatment, and the post-test. The speaking test administered to the students of experimental and control class. The scores were assessed by three assessors. The data were analyzed and interpreted by using Pearson Product Moment assisted by SPSS 20 (Statistical Package for Service Solution) software program. The result of the research reveals that application of drama technique can be proven to develop students’ speaking skill and the application of drama technique develop students’ speaking skill. The result of the students’ speaking score development from pre-test to post test demonstrates a significant difference. The researcher found that sig.(2 tailed) is 0.000 which is lower than 0.05. The outcome of the students’ speaking development comparison between experimental and control group is 0.025. It means that the development is significant. The application of drama technique developed and improve students’ speaking skill. The finding signifies that application of drama technique is responded positively by the experimental class.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".